A direct algorithm for joint optimal sensor scheduling and MAP state estimation for hidden Markov models
David M. Jun, David M. Cohen, Douglas L. Jones · 2013
Sensing systems with multiple sensors and operating modes warrant active management techniques to balance estimation quality and measurement costs. Existing literature shows that in the joint sensor-scheduling and state-estimation problem for HMMs, estimator optimization can be done independently of the scheduler at each time step. We investigate the special case when a MAP estimator is used, and show how the joint problem can be converted to a standard Partially Observable MarkovDecision Process (POMDP), which in turn enables us to use POMDP solvers. As this approach is highly redundant, we derive a direct solution, which exploits the separability property while still utilizing standard solvers. When compared to standard techniques, the direct algorithm provides savings by a factor of the state-space dimension. Numerical results are given for an example motivated by wildlife monitoring.